The confidence intervals will still be available, at a greater
computational cost though.
Indeed if you ask for prediction variance using eval_MSE, the GP class will
compute the cross-distances and the covariance again from X.
2014-03-06 19:20 GMT+01:00 Ralf Gunter :
> Thanks Vincent, that indee
Hi Layton.
Thanks for the feedback. I shall make that a bit stronger. Thanks.
On Fri, Mar 7, 2014 at 6:03 AM, Robert Layton wrote:
> I agree, it is a strong project and important stuff to do. I feel that the
> motivation is lacking purpose -- why bother doing this project? (that's not
> a rhet
I agree, it is a strong project and important stuff to do. I feel that the
motivation is lacking purpose -- why bother doing this project? (that's not
a rhetorical question). At present, the project feels like "here are some
things to do, so I'll do them", without any real reason why they should be
hi Manoj,
looks like a pretty decent proposal to me.
Cheers,
Alex
On Thu, Mar 6, 2014 at 6:41 PM, Manoj Kumar
wrote:
> Hello,
>
> I have prepared a wiki page for the first draft of my GSoC proposal after
> several discussions. Please do have a look and provide me feedback.
> https://github.com
Thanks Vincent, that indeed does the trick! It would be very useful to
have the confidence intervals along as well, but this should do
meanwhile.
It's odd that only the compressed code path is hitting this bug, since
I'd imagine both versions are serializing the object to a "s#" string
at some poi
Hello,
I have prepared a wiki page for the first draft of my GSoC proposal after
several discussions. Please do have a look and provide me feedback.
https://github.com/scikit-learn/scikit-learn/wiki/GSoC-2014-Application:-Improved-Linear-Models
Thanks
--
Regards,
Manoj Kumar,
Mech Undergrad
http
Hi Ralf,
The GaussianProcess class computes and stores the full matrix of Manhattan
distances between features hence the object can quickly take a huge amount
of memory...
One option though consists in dumping this big matrix after fit by using
the storage_mode='light' kwarg (default is 'full' and
Hi Maheshakya,
In regular LSH, a particular setting of the number of hash
functions per index (k) and the number of indexes (L) essentially
determines the size of the region in space from which candidates
will be chosen in response to a query. If queries q1
